Abstract

This session aims to promote shifting the focus of statistics instruction for non-majors towards more wholistic data literacy outcomes. Case studies, lessons learned, and measurement tools that can serve the field will be presented. Technological developments have led to the creation of unprecedented amounts of data, and a cultural shift whereby decisions are almost exclusively data driven. Whether conducting interviews, working with organizational performance indicators, tracking social media behaviour, or reading charts and dashboards, individuals working in fields such as marketing, politics, healthcare, and program evaluation are interacting with exponentially more data and data-based products than ever before. Data literacy is the ability to interpret, critically evaluate, and communicate the statistical-, text-, and probability-based information that emerges in a data-driven society. It requires knowledge of statistics, but also elements of data science, information science, and beyond. Currently, formal training in statistics courses focuses primarily on the mathematical processes and underlying statistical analyses, leaving many students – especially non-majors – overwhelmed and without the understanding of core concepts necessary for identifying good and bad representations of data, converting this knowledge into action, and succeeding in their future careers. To better prepare the current generation of workers for the job market and to help promote productive citizenship, higher education statistics instruction should shift focus towards data literacy outcomes.

The speakers will present research results examining the current prevalence of data literacy in higher education, as well as collaborative projects and design principles for revised courses aiming to promote data literacy outcomes. Perspectives from multiple stakeholders including students, professors, and employers obtained using a variety of cross-sectional and longitudinal research designs will be shared. These results will highlight the low rates of data literacy outcomes for students, lack of preparedness for the job market, challenges in current statistics courses, and opportunities for improvements moving forward. The session will conclude with a critical overview of implications for curriculum and training development, as well as key challenges and future directions, and a general discussion.